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|Title:||Enhanced generalization of nonparametric model for magnetorheological dampers||Other Title:||磁流变阻尼器非参数化模型泛化能力的提高||Authors:||Chen, Z
|Issue Date:||2017||Source:||振动与冲击 (Journal of vibration and shock), 2017, v. 36, no. 6, p. 146-151 and 167||Abstract:||建立磁流变阻尼器的动态模型以描述其强非线性动力学行为是智能磁流变控制系统设计及应用的关键环节之一。泛化能力是衡量基于人工神经网络技术的磁流变阻尼器非参数化模型性能的重要指标,也是保证控制系统稳定性和可靠性的重要因素。基于磁流变阻尼器的动力学试验数据,提出贝叶斯推理分析框架下的非线性自回归(nonlinear autoregressive with exogenous inputs,NARX)神经网络技术建立磁流变阻尼器的动态模型,通过网络结构优化和正则化学习算法的结合以有效地提高模型的预测精度和泛化能力。研究结果表明,基于贝叶斯推理的NARX网络模型能够准确地预测磁流变阻尼器在周期和随机激励下的非线性动态行为,同时验证了该模型相比于非正则化模型在泛化性能方面的优越性,因此,有利于实现磁流变控制系统的实时、鲁棒智能化控制。
The dynamic modeling for magnetorheological (MR) dampers to describe their highly nonlinear dynamic characteristics is essential for the design and implementation of a smart MR control system. One critical concern in constructing a nonparametric MR damper model by employing the artificial neural network technique is its generalization capability, which is also significant to guarantee the stability and reliability of the MR control system. The paper presents the modeling of MR dampers with the employment of the NARX (nonlinear autoregressive with exogenous inputs) network technique within a Bayesian inference framework, and addresses the enhancement of model prediction accuracy and generalization capability in terms of the network architecture optimization and regularized network learning algorithm. The Bayesian regularized NARX network model for the MR damper is demonstrated to outperform the non-regularized network model with the superior prediction and generalization performance in the scenarios of harmonic and random excitations. Therefore, the proposed model with enhanced generalization is beneficial to realize the real-time and robust smart control of MR systems.
|Publisher:||中國學術期刊 (光盤版) 電子雜誌社||Journal:||振动与冲击 (Journal of vibration and shock)||ISSN:||1000-3835||DOI:||10.13465/j.cnki.jvs.2017.06.022||Rights:||© 2017 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.
© 2017 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
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